Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images

Åhlén, Julia; Sundgren, David

doi:10.1007/3-540-45103-x_121

Cited by 7 publications

(6 citation statements)

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“…To calculate the contrast in a region of a color image having pixels, we use red green blue red green blue (17) where is an index of the chromatic channel and (18) The contrast of the effective object and the sensed image are calculated. Relative to the contrast of the original scene, the degradations of contrast are and (19) Before inspecting these values, we define an image resulting from all degradation effects excluding blur (20) with a respective contrast degradation measure (21) 4 The weight of the veiling light relative to the signal depends on the object albedo. The larger the albedo, the stronger the signal is.…”

Section: The Dominant Degradation Componentmentioning

confidence: 99%

“…9 is rather white. If the scene does not include such a natural object, then a white target can be introduced to the scenery [20]. Suppose that we normalize the color of the …”

Section: A Naive Attempt For Color Correctionmentioning

confidence: 99%

“…As in conventional computer vision, algorithms are sought for navigation and control [19] of submerged robots. In addition, underwater imaging is used for research in marine biology [6], [20]- [22], archaeology [1], [23]- [25], and mapping [19]. Moreover, underwater photography [26], [27] is becoming more accessible to a wider public.…”

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confidence: 99%

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Recovery of Underwater Visibility and Structure by Polarization Analysis

Schechner

Karpel

2005

IEEE J. Oceanic Eng.

532

263

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Abstract-Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.Index Terms-Color, illumination, image enhancement, inverse problems, polarized light, scattering, three-dimensional reconstruction, undersea vision, underwater imaging. I. UNDERWATER VISION UNDERWATER vision is plagued by poor visibility conditions [1]- [6]. According to [7], most computer vision methods (e.g., those based on stereo triangulation or on structure from motion) cannot be employed directly underwater. This is due to the particularly challenging environmental conditions that complicate image matching and analysis. It is important to alleviate these visibility problems since underwater imaging is widely used in scientific research and technology. Computer vision methods are being used in this mode of imaging for various applications [5] What makes underwater imaging so problematic? To understand the challenge, consider Fig. 1. which shows an underwater archaeological site about 2.5-m deep. It is easy to see that visibility degradation effects vary as distances to objects increase [3], [28]. Since objects in the field of view (FOV) are at different distances from the camera, the causes for image degradation are spatially varying. This situation is analogous to open-air vision in bad weather (fog or haze) described in [29]-[34]. Contrary to this fact, traditional image enhancement tools, e.g., high pass filtering and histogram equalization, are typically spatially invariant. Since they do not model the spatially varying distance dependencies, traditional methods are of limited utility in countering visibility problems, as has been demonstrated in past experiments [33], [35] as well as in this paper.In this paper, we develop a physics-based approach for recovery of visibility when imaging underwater scenes in natural illumination. Since it is based on the models of image formation, the approach automatically accounts for dependencies on object distance and estimates a distance map of the scene as a by-product. The approach is fast and relies on raw images taken through different states of a polarizing filter. 1 These raw images have...

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Section: The Dominant Degradation Componentmentioning

confidence: 99%

“…9 is rather white. If the scene does not include such a natural object, then a white target can be introduced to the scenery [20]. Suppose that we normalize the color of the …”

Section: A Naive Attempt For Color Correctionmentioning

confidence: 99%

See 1 more Smart Citation

Recovery of Underwater Visibility and Structure by Polarization Analysis

Schechner

Karpel

2005

IEEE J. Oceanic Eng.

532

263

View full text Add to dashboard Cite

show abstract

“…As in conventional computer vision, algorithms are sought for navigation and control [37] of submerged robots. In addition, underwater imaging is used for research in marine biology [2,7,13,36], archaeology [15] and mapping [37]. Moreover, underwater photography [34] is becoming more accessible to the wider public.…”

Section: Underwater Visionmentioning

confidence: 99%

“…As a light ray progresses from the object towards the camera, part of its energy is lost due to scattering and absorption. The fraction reaching the camera is the direct transmission 2 2 There is a proportion factor between the scene radiance and image irradiance that depends on the imaging system, but does not depend on the Figure 2. The optical manhole.…”

Section: Direct Transmissionmentioning

confidence: 99%